Notice bibliographique
Résumé
Higher education related to water is a critical component of capacity development necessary to support countries’ progress towards Sustainable Development Goals (SDGs) overall, and towards the SDG6 water and sanitation goal in particular. Although the precise number is unknown, there are at least 28,000 higher education institutions in the world. The actual number is likely higher and constantly changing. Water education programmes are very diverse and complex and can include components of engineering, biology, chemistry, physics, hydrology, hydrogeology, ecology, geography, earth sciences, public health, sociology, law, and political sciences, to mention a few areas. In addition, various levels of qualifications are offered, ranging from certificate, diploma, baccalaureate, to the master’s and doctorate (or equivalent) levels. The percentage of universities offering programmes in ‘water’ ranges from 40% in the USA and Europe to 1% in subSaharan Africa. There are no specific data sets available for the extent or quality of teaching ‘water’ in universities. Consequently, insights on this have to be drawn or inferred from data sources on overall research and teaching excellence such as Scopus, the Shanghai Academic Ranking of World Universities, the Times Higher Education, the Ranking Web of Universities, the Our World in Data website and the UN Statistics Division data. Using a combination of measures of research excellence in water resources and related topics, and overall rankings of university teaching excellence, universities with representation in both categories were identified. Very few universities are represented in both categories. Countries that have at least three universities in the list of the top 50 include USA, Australia, China, UK, Netherlands and Canada. There are universities that have excellent reputations for both teaching excellence and for excellent and diverse research activities in water-related topics. They are mainly in the USA, Europe, Australia and China. Other universities scored well on research in water resources but did not in teaching excellence. The approach proposed in this report has potential to guide the development of comprehensive programmes in water. No specific comparative data on the quality of teaching in water-related topics has been identified. This report further shows the variety of pathways which most water education programmes are associated with or built in – through science, technology and engineering post-secondary and professional education systems. The multitude of possible institutions and pathways to acquire a qualification in water means that a better ‘roadmap’ is needed to chart the programmes. A global database with details on programme curricula, qualifications offered, duration, prerequisites, cost, transfer opportunities and other programme parameters would be ideal for this purpose, showing country-level, regional and global search capabilities. Cooperation between institutions in preparing or presenting water programmes is currently rather limited. Regional consortia of institutions may facilitate cooperation. A similar process could be used for technical and vocational education and training, although a more local approach would be better since conditions, regulations and technologies vary between relatively small areas. Finally, this report examines various factors affecting the future availability of water professionals. This includes the availability of suitable education and training programmes, choices that students make to pursue different areas of study, employment prospects, increasing gender equity, costs of education, and students’ and graduates’ mobility, especially between developing and developed countries. This report aims to inform and open a conversation with educators and administrators in higher education especially those engaged in water education or preparing to enter that field. It will also benefit students intending to enter the water resources field, professionals seeking an overview of educational activities for continuing education on water and government officials and politicians responsible for educational activities
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,012 | 0,038 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,004 | 0,006 |
| Science ouverte | 0,001 | 0,003 |
| Intégrité de la recherche | 0,002 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,013 | 0,004 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».